Today, the Internet represents a vital information resource for both the sophisticated user and the novice. The popularity of the Internet as an information source is due, in part, to the vast amount of information available and to the relative ease with which that information may be accessed. However, the enormity of the information available to user does not alone address the dynamically-evolving needs and interests of users of the Internet.
Many web sites, and in particular, those associated with online news organizations, blogs, and content providers, provide pages or links to popular content that may be relevant to a user. For example, a news web site may recommend links to stories, images, and videos that are popular with users who have viewed the organization's web site. It has been empirically proven that delivering personalized content that is most likely to interest users visiting the web site plays a key role in attracting more users.
Some online media outlets may feature or implement recommendation modules that deliver personalized content to users. However, these conventional recommendation modules provide either general recommendations or provide recommendations for electronic content based on selected categories or key words and/or profiles that are associated with a user. Moreover, many fail to provide or update specific recommendations for electronic content based on real time events, data, and/or trends associated with a user.
In view of the foregoing, there is a need for improved systems and methods for recommending electronic content to a user. Moreover, there is a need for improved techniques for providing personalized recommendations for electronic content and updating such recommendations for users to address, for example, real time or dynamic data, events, and/or trends.